{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ConvolutionalNetworks.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QFbCC-2M3Jv4","executionInfo":{"status":"ok","timestamp":1612876682216,"user_tz":-60,"elapsed":19766,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"d8d47e44-4663-4ba7-bcdd-375057f1dfee"},"source":["# this mounts your Google Drive to the Colab VM.\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)\n","\n","# enter the foldername in your Drive where you have saved the unzipped\n","# assignment folder, e.g. 'cs231n/assignments/assignment3/'\n","FOLDERNAME = 'cs231n/assignments/assignment2/'\n","assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n","\n","# now that we've mounted your Drive, this ensures that\n","# the Python interpreter of the Colab VM can load\n","# python files from within it.\n","import sys\n","sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n","\n","# this downloads the CIFAR-10 dataset to your Drive\n","# if it doesn't already exist.\n","%cd drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n","!bash get_datasets.sh\n","%cd /content"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n","/content/drive/My Drive/cs231n/assignments/assignment2/cs231n/datasets\n","/content\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"tags":["pdf-title"],"id":"nhUCjU-O3Jv_"},"source":["# Convolutional Networks\n","\n","So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n","\n","First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore"],"id":"Xn6cRqKl3JwA","executionInfo":{"status":"ok","timestamp":1612876691681,"user_tz":-60,"elapsed":4942,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}}},"source":["# As usual, a bit of setup\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from cs231n.classifiers.cnn import *\n","from cs231n.data_utils import get_CIFAR10_data\n","from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n","from cs231n.layers import *\n","from cs231n.fast_layers import *\n","from cs231n.solver import Solver\n","\n","%matplotlib inline\n","plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n","plt.rcParams['image.interpolation'] = 'nearest'\n","plt.rcParams['image.cmap'] = 'gray'\n","\n","# for auto-reloading external modules\n","# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n","%load_ext autoreload\n","%autoreload 2\n","\n","def rel_error(x, y):\n","  \"\"\" returns relative error \"\"\"\n","  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"tags":["pdf-ignore"],"colab":{"base_uri":"https://localhost:8080/"},"id":"IyufzEZK3JwB","executionInfo":{"status":"ok","timestamp":1612876698094,"user_tz":-60,"elapsed":6401,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"37003256-36e5-4856-9b10-419be4251266"},"source":["# Load the (preprocessed) CIFAR10 data.\n","\n","data = get_CIFAR10_data()\n","for k, v in data.items():\n","  print('%s: ' % k, v.shape)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["X_train:  (49000, 3, 32, 32)\n","y_train:  (49000,)\n","X_val:  (1000, 3, 32, 32)\n","y_val:  (1000,)\n","X_test:  (1000, 3, 32, 32)\n","y_test:  (1000,)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"o4HEWSWV3JwB"},"source":["# Convolution: Naive forward pass\n","The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n","\n","You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n","\n","You can test your implementation by running the following:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ny8zvbdc3JwC","executionInfo":{"status":"ok","timestamp":1612876698095,"user_tz":-60,"elapsed":5376,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"b43920fc-ba6d-49bd-c5e9-0a8f0048ddf6"},"source":["x_shape = (2, 3, 4, 4)\n","w_shape = (3, 3, 4, 4)\n","x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n","w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n","b = np.linspace(-0.1, 0.2, num=3)\n","\n","conv_param = {'stride': 2, 'pad': 1}\n","out, _ = conv_forward_naive(x, w, b, conv_param)\n","correct_out = np.array([[[[-0.08759809, -0.10987781],\n","                           [-0.18387192, -0.2109216 ]],\n","                          [[ 0.21027089,  0.21661097],\n","                           [ 0.22847626,  0.23004637]],\n","                          [[ 0.50813986,  0.54309974],\n","                           [ 0.64082444,  0.67101435]]],\n","                         [[[-0.98053589, -1.03143541],\n","                           [-1.19128892, -1.24695841]],\n","                          [[ 0.69108355,  0.66880383],\n","                           [ 0.59480972,  0.56776003]],\n","                          [[ 2.36270298,  2.36904306],\n","                           [ 2.38090835,  2.38247847]]]])\n","\n","# Compare your output to ours; difference should be around e-8\n","print('Testing conv_forward_naive')\n","print('difference: ', rel_error(out, correct_out))"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Testing conv_forward_naive\n","difference:  2.2121476417505994e-08\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lOT93dc93JwC"},"source":["# Aside: Image processing via convolutions\n","\n","As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."]},{"cell_type":"markdown","metadata":{"id":"NFl_nzrJ3JwD"},"source":["## Colab Users Only\n","\n","Please execute the below cell to copy two cat images to the Colab VM."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lv2TSqoa3JwD","executionInfo":{"status":"ok","timestamp":1612876699101,"user_tz":-60,"elapsed":1928,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"a6d9cfa8-39b2-4399-e361-3d792e8c9c31"},"source":["# Colab users only!\n","%mkdir -p cs231n/notebook_images\n","%cd drive/My\\ Drive/$FOLDERNAME/cs231n\n","%cp -r notebook_images/ /content/cs231n/\n","%cd /content/"],"execution_count":5,"outputs":[{"output_type":"stream","text":["/content/drive/My Drive/cs231n/assignments/assignment2/cs231n\n","/content\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"8jDmJlrU3JwD","executionInfo":{"status":"ok","timestamp":1612876701206,"user_tz":-60,"elapsed":3213,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"be02b57a-c58e-42bb-f3ff-bd3eca97b748"},"source":["from imageio import imread\n","from PIL import Image\n","\n","kitten = imread('cs231n/notebook_images/kitten.jpg')\n","puppy = imread('cs231n/notebook_images/puppy.jpg')\n","# kitten is wide, and puppy is already square\n","d = kitten.shape[1] - kitten.shape[0]\n","kitten_cropped = kitten[:, d//2:-d//2, :]\n","\n","img_size = 200   # Make this smaller if it runs too slow\n","resized_puppy = np.array(Image.fromarray(puppy).resize((img_size, img_size)))\n","resized_kitten = np.array(Image.fromarray(kitten_cropped).resize((img_size, img_size)))\n","x = np.zeros((2, 3, img_size, img_size))\n","x[0, :, :, :] = resized_puppy.transpose((2, 0, 1))\n","x[1, :, :, :] = resized_kitten.transpose((2, 0, 1))\n","\n","# Set up a convolutional weights holding 2 filters, each 3x3\n","w = np.zeros((2, 3, 3, 3))\n","\n","# The first filter converts the image to grayscale.\n","# Set up the red, green, and blue channels of the filter.\n","w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n","w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n","w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n","\n","# Second filter detects horizontal edges in the blue channel.\n","w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n","\n","# Vector of biases. We don't need any bias for the grayscale\n","# filter, but for the edge detection filter we want to add 128\n","# to each output so that nothing is negative.\n","b = np.array([0, 128])\n","\n","# Compute the result of convolving each input in x with each filter in w,\n","# offsetting by b, and storing the results in out.\n","out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n","\n","def imshow_no_ax(img, normalize=True):\n","    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n","    if normalize:\n","        img_max, img_min = np.max(img), np.min(img)\n","        img = 255.0 * (img - img_min) / (img_max - img_min)\n","    plt.imshow(img.astype('uint8'))\n","    plt.gca().axis('off')\n","\n","# Show the original images and the results of the conv operation\n","plt.subplot(2, 3, 1)\n","imshow_no_ax(puppy, normalize=False)\n","plt.title('Original image')\n","plt.subplot(2, 3, 2)\n","imshow_no_ax(out[0, 0])\n","plt.title('Grayscale')\n","plt.subplot(2, 3, 3)\n","imshow_no_ax(out[0, 1])\n","plt.title('Edges')\n","plt.subplot(2, 3, 4)\n","imshow_no_ax(kitten_cropped, normalize=False)\n","plt.subplot(2, 3, 5)\n","imshow_no_ax(out[1, 0])\n","plt.subplot(2, 3, 6)\n","imshow_no_ax(out[1, 1])\n","plt.show()"],"execution_count":6,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 6 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"DmR8sb0N3JwE"},"source":["# Convolution: Naive backward pass\n","Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n","\n","When you are done, run the following to check your backward pass with a numeric gradient check."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nSP0IqeC3JwF","executionInfo":{"status":"ok","timestamp":1612818259699,"user_tz":-60,"elapsed":6804,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"e2f329c2-7286-458b-e31a-57771ecdd405"},"source":["np.random.seed(231)\n","x = np.random.randn(4, 3, 5, 5)\n","w = np.random.randn(2, 3, 3, 3)\n","b = np.random.randn(2,)\n","dout = np.random.randn(4, 2, 5, 5)\n","conv_param = {'stride': 1, 'pad': 1}\n","\n","dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n","dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n","db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n","\n","out, cache = conv_forward_naive(x, w, b, conv_param)\n","dx, dw, db = conv_backward_naive(dout, cache)\n","\n","# Your errors should be around e-8 or less.\n","print('Testing conv_backward_naive function')\n","print('dx error: ', rel_error(dx, dx_num))\n","print('dw error: ', rel_error(dw, dw_num))\n","print('db error: ', rel_error(db, db_num))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Testing conv_backward_naive function\n","dx error:  1.159803161159293e-08\n","dw error:  2.2471264748452487e-10\n","db error:  3.37264006649648e-11\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"pXquGQqp3JwF"},"source":["# Max-Pooling: Naive forward\n","Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n","\n","Check your implementation by running the following:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4ly0ylfN3JwF","executionInfo":{"status":"ok","timestamp":1612818259701,"user_tz":-60,"elapsed":5147,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"9c6a824d-24ad-47f9-e127-fb9018f66ff3"},"source":["x_shape = (2, 3, 4, 4)\n","x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n","pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n","\n","out, _ = max_pool_forward_naive(x, pool_param)\n","\n","correct_out = np.array([[[[-0.26315789, -0.24842105],\n","                          [-0.20421053, -0.18947368]],\n","                         [[-0.14526316, -0.13052632],\n","                          [-0.08631579, -0.07157895]],\n","                         [[-0.02736842, -0.01263158],\n","                          [ 0.03157895,  0.04631579]]],\n","                        [[[ 0.09052632,  0.10526316],\n","                          [ 0.14947368,  0.16421053]],\n","                         [[ 0.20842105,  0.22315789],\n","                          [ 0.26736842,  0.28210526]],\n","                         [[ 0.32631579,  0.34105263],\n","                          [ 0.38526316,  0.4       ]]]])\n","\n","# Compare your output with ours. Difference should be on the order of e-8.\n","print('Testing max_pool_forward_naive function:')\n","print('difference: ', rel_error(out, correct_out))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Testing max_pool_forward_naive function:\n","difference:  4.1666665157267834e-08\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"imiJmJDD3JwG"},"source":["# Max-Pooling: Naive backward\n","Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n","\n","Check your implementation with numeric gradient checking by running the following:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0GYm133A3JwH","executionInfo":{"status":"ok","timestamp":1612818260204,"user_tz":-60,"elapsed":3990,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"9c81d3c1-6b3c-4117-e52d-8f7506fa7cf3"},"source":["np.random.seed(231)\n","x = np.random.randn(3, 2, 8, 8)\n","dout = np.random.randn(3, 2, 4, 4)\n","pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n","\n","dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n","\n","out, cache = max_pool_forward_naive(x, pool_param)\n","dx = max_pool_backward_naive(dout, cache)\n","\n","# Your error should be on the order of e-12\n","print('Testing max_pool_backward_naive function:')\n","print('dx error: ', rel_error(dx, dx_num))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Testing max_pool_backward_naive function:\n","dx error:  3.27562514223145e-12\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"j2ZrQqAY3JwH"},"source":["# Fast layers\n","\n","Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`."]},{"cell_type":"markdown","metadata":{"id":"f2wj5TMr3JwH"},"source":["The fast convolution implementation depends on a Cython extension; to compile it either execute the local development cell (option A) if you are developing locally, or the Colab cell (option B) if you are running this assignment in Colab.\n","\n","---\n","\n","**Very Important, Please Read**. For **both** option A and B, you have to **restart** the notebook after compiling the cython extension. In Colab, please save the notebook `File -> Save`, then click `Runtime -> Restart Runtime -> Yes`. This will restart the kernel which means local variables will be lost. Just re-execute the cells from top to bottom and skip the cell below as you only need to run it once for the compilation step.\n","\n","---"]},{"cell_type":"markdown","metadata":{"id":"89osNLXR3JwI"},"source":["## Option A: Local Development\n","\n","Go to the cs231n directory and execute the following in your terminal:\n","\n","```bash\n","python setup.py build_ext --inplace\n","```"]},{"cell_type":"markdown","metadata":{"id":"2zlDQOTH3JwI"},"source":["## Option B: Colab\n","\n","Execute the cell below only only **ONCE**."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9sgWYtC13JwI","executionInfo":{"status":"ok","timestamp":1612734730476,"user_tz":-60,"elapsed":10435,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"5388788c-c6c3-4b84-a4a0-5e65a3a5ce28"},"source":["%cd drive/My\\ Drive/$FOLDERNAME/cs231n/\n","!python setup.py build_ext --inplace"],"execution_count":null,"outputs":[{"output_type":"stream","text":["=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n","\tYou will need to compile a Cython extension for a portion of this assignment.\n","\tThe instructions to do this will be given in a section of the notebook below.\n","\tThere will be an option for Colab users and another for Jupyter (local) users.\n","/content/drive/My Drive/cs231n/assignments/assignment2/cs231n\n","Compiling im2col_cython.pyx because it changed.\n","[1/1] Cythonizing im2col_cython.pyx\n","/usr/local/lib/python3.6/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/drive/My Drive/cs231n/assignments/assignment2/cs231n/im2col_cython.pyx\n","  tree = Parsing.p_module(s, pxd, full_module_name)\n","running build_ext\n","building 'im2col_cython' extension\n","creating build\n","creating build/temp.linux-x86_64-3.6\n","x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I/usr/include/python3.6m -c im2col_cython.c -o build/temp.linux-x86_64-3.6/im2col_cython.o\n","In file included from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/arrayobject.h:4\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kim2col_cython.c:629\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K-Wcpp\u001b[m\u001b[K]\n"," #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n","  \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n","x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/im2col_cython.o -o /content/drive/My Drive/cs231n/assignments/assignment2/cs231n/im2col_cython.cpython-36m-x86_64-linux-gnu.so\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"hXpuHcgm3JwI"},"source":["The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n","\n","**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n","\n","You can compare the performance of the naive and fast versions of these layers by running the following:"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"KBnpWL5S3JwI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612876725468,"user_tz":-60,"elapsed":11928,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"2a33a193-87e8-4eff-e790-0e83a56a0de3"},"source":["# Rel errors should be around e-9 or less\n","from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n","from time import time\n","np.random.seed(231)\n","x = np.random.randn(100, 3, 31, 31)\n","w = np.random.randn(25, 3, 3, 3)\n","b = np.random.randn(25,)\n","dout = np.random.randn(100, 25, 16, 16)\n","conv_param = {'stride': 2, 'pad': 1}\n","\n","t0 = time()\n","out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n","t1 = time()\n","out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n","t2 = time()\n","\n","print('Testing conv_forward_fast:')\n","print('Naive: %fs' % (t1 - t0))\n","print('Fast: %fs' % (t2 - t1))\n","print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n","print('Difference: ', rel_error(out_naive, out_fast))\n","\n","t0 = time()\n","dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n","t1 = time()\n","dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n","t2 = time()\n","\n","print('\\nTesting conv_backward_fast:')\n","print('Naive: %fs' % (t1 - t0))\n","print('Fast: %fs' % (t2 - t1))\n","print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n","print('dx difference: ', rel_error(dx_naive, dx_fast))\n","print('dw difference: ', rel_error(dw_naive, dw_fast))\n","print('db difference: ', rel_error(db_naive, db_fast))"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Testing conv_forward_fast:\n","Naive: 3.528322s\n","Fast: 0.010437s\n","Speedup: 338.074131x\n","Difference:  4.926407851494105e-11\n","\n","Testing conv_backward_fast:\n","Naive: 7.174038s\n","Fast: 0.014395s\n","Speedup: 498.386683x\n","dx difference:  1.949764775345631e-11\n","dw difference:  3.681156828004736e-13\n","db difference:  3.481354613192702e-14\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"srGWpq4I3JwJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612876728216,"user_tz":-60,"elapsed":2735,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"a11efa01-4a22-4e1e-beef-01725d6197c8"},"source":["# Relative errors should be close to 0.0\n","from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n","np.random.seed(231)\n","x = np.random.randn(100, 3, 32, 32)\n","dout = np.random.randn(100, 3, 16, 16)\n","pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n","\n","t0 = time()\n","out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n","t1 = time()\n","out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n","t2 = time()\n","\n","print('Testing pool_forward_fast:')\n","print('Naive: %fs' % (t1 - t0))\n","print('fast: %fs' % (t2 - t1))\n","print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n","print('difference: ', rel_error(out_naive, out_fast))\n","\n","t0 = time()\n","dx_naive = max_pool_backward_naive(dout, cache_naive)\n","t1 = time()\n","dx_fast = max_pool_backward_fast(dout, cache_fast)\n","t2 = time()\n","\n","print('\\nTesting pool_backward_fast:')\n","print('Naive: %fs' % (t1 - t0))\n","print('fast: %fs' % (t2 - t1))\n","print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n","print('dx difference: ', rel_error(dx_naive, dx_fast))"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Testing pool_forward_fast:\n","Naive: 0.290965s\n","fast: 0.002990s\n","speedup: 97.320175x\n","difference:  0.0\n","\n","Testing pool_backward_fast:\n","Naive: 0.631516s\n","fast: 0.013132s\n","speedup: 48.089506x\n","dx difference:  0.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GoN9sVZM3JwJ"},"source":["# Convolutional \"sandwich\" layers\n","Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks. Run the cells below to sanity check they're working."]},{"cell_type":"code","metadata":{"id":"jf_9Ncqt3JwJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612876730369,"user_tz":-60,"elapsed":1540,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"9d7305b1-cfae-4422-d8ed-8dd69312d151"},"source":["from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n","np.random.seed(231)\n","x = np.random.randn(2, 3, 16, 16)\n","w = np.random.randn(3, 3, 3, 3)\n","b = np.random.randn(3,)\n","dout = np.random.randn(2, 3, 8, 8)\n","conv_param = {'stride': 1, 'pad': 1}\n","pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n","\n","out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n","dx, dw, db = conv_relu_pool_backward(dout, cache)\n","\n","dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n","dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n","db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n","\n","# Relative errors should be around e-8 or less\n","print('Testing conv_relu_pool')\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dw error: ', rel_error(dw_num, dw))\n","print('db error: ', rel_error(db_num, db))"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Testing conv_relu_pool\n","dx error:  9.591132621921372e-09\n","dw error:  5.802391137330214e-09\n","db error:  1.0146343411762047e-09\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Hb_wMTtP3JwJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612876734383,"user_tz":-60,"elapsed":616,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"8d047c55-9d58-4eac-d09f-d52ac9e1db0c"},"source":["from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n","np.random.seed(231)\n","x = np.random.randn(2, 3, 8, 8)\n","w = np.random.randn(3, 3, 3, 3)\n","b = np.random.randn(3,)\n","dout = np.random.randn(2, 3, 8, 8)\n","conv_param = {'stride': 1, 'pad': 1}\n","\n","out, cache = conv_relu_forward(x, w, b, conv_param)\n","dx, dw, db = conv_relu_backward(dout, cache)\n","\n","dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n","dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n","db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n","\n","# Relative errors should be around e-8 or less\n","print('Testing conv_relu:')\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dw error: ', rel_error(dw_num, dw))\n","print('db error: ', rel_error(db_num, db))"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Testing conv_relu:\n","dx error:  1.5218619980349303e-09\n","dw error:  2.702022646099404e-10\n","db error:  1.451272393591721e-10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"li4MbrXQ3JwJ"},"source":["# Three-layer ConvNet\n","Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n","\n","Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:"]},{"cell_type":"markdown","metadata":{"id":"7BbtzKPl3JwK"},"source":["## Sanity check loss\n","After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization the loss should go up slightly."]},{"cell_type":"code","metadata":{"id":"o0TcBVSn3JwK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612793117744,"user_tz":-60,"elapsed":2224,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"e460f79e-61b4-44bc-8ca7-500fc10a3236"},"source":["model = ThreeLayerConvNet()\n","\n","N = 50\n","X = np.random.randn(N, 3, 32, 32)\n","y = np.random.randint(10, size=N)\n","\n","loss, grads = model.loss(X, y)\n","print('Initial loss (no regularization): ', loss)\n","\n","model.reg = 0.5\n","loss, grads = model.loss(X, y)\n","print('Initial loss (with regularization): ', loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Initial loss (no regularization):  2.3025850635890874\n","Initial loss (with regularization):  2.508600069296828\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eggHIiNY3JwK"},"source":["## Gradient check\n","After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2."]},{"cell_type":"code","metadata":{"id":"WhS9Aybr3JwK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794196376,"user_tz":-60,"elapsed":3510,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"51f2aa34-eaf3-4bbb-ce86-c7f3f2cc502d"},"source":["num_inputs = 2\n","input_dim = (3, 16, 16)\n","reg = 0.0\n","num_classes = 10\n","np.random.seed(231)\n","X = np.random.randn(num_inputs, *input_dim)\n","y = np.random.randint(num_classes, size=num_inputs)\n","\n","model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n","                          input_dim=input_dim, hidden_dim=7,\n","                          dtype=np.float64)\n","loss, grads = model.loss(X, y)\n","# Errors should be small, but correct implementations may have\n","# relative errors up to the order of e-2\n","for param_name in sorted(grads):\n","    f = lambda _: model.loss(X, y)[0]\n","    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n","    e = rel_error(param_grad_num, grads[param_name])\n","    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["W1 max relative error: 1.380104e-04\n","W2 max relative error: 1.822723e-02\n","W3 max relative error: 3.064049e-04\n","b1 max relative error: 3.477652e-05\n","b2 max relative error: 2.516375e-03\n","b3 max relative error: 7.945660e-10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CLa6dWiH3JwK"},"source":["## Overfit small data\n","A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."]},{"cell_type":"code","metadata":{"id":"yQbv3Wa63JwL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794266837,"user_tz":-60,"elapsed":40564,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"d4123cf6-e6e3-4144-e681-3673feb60d92"},"source":["np.random.seed(231)\n","\n","num_train = 100\n","small_data = {\n","  'X_train': data['X_train'][:num_train],\n","  'y_train': data['y_train'][:num_train],\n","  'X_val': data['X_val'],\n","  'y_val': data['y_val'],\n","}\n","\n","model = ThreeLayerConvNet(weight_scale=1e-2)\n","\n","solver = Solver(model, small_data,\n","                num_epochs=15, batch_size=50,\n","                update_rule='adam',\n","                optim_config={\n","                  'learning_rate': 1e-3,\n","                },\n","                verbose=True, print_every=1)\n","solver.train()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(Iteration 1 / 30) loss: 2.414060\n","(Epoch 0 / 15) train acc: 0.200000; val_acc: 0.137000\n","(Iteration 2 / 30) loss: 3.102925\n","(Epoch 1 / 15) train acc: 0.140000; val_acc: 0.087000\n","(Iteration 3 / 30) loss: 2.270330\n","(Iteration 4 / 30) loss: 2.096705\n","(Epoch 2 / 15) train acc: 0.240000; val_acc: 0.094000\n","(Iteration 5 / 30) loss: 1.838880\n","(Iteration 6 / 30) loss: 1.934188\n","(Epoch 3 / 15) train acc: 0.510000; val_acc: 0.173000\n","(Iteration 7 / 30) loss: 1.827912\n","(Iteration 8 / 30) loss: 1.639574\n","(Epoch 4 / 15) train acc: 0.520000; val_acc: 0.188000\n","(Iteration 9 / 30) loss: 1.330082\n","(Iteration 10 / 30) loss: 1.756115\n","(Epoch 5 / 15) train acc: 0.630000; val_acc: 0.167000\n","(Iteration 11 / 30) loss: 1.024162\n","(Iteration 12 / 30) loss: 1.041826\n","(Epoch 6 / 15) train acc: 0.750000; val_acc: 0.229000\n","(Iteration 13 / 30) loss: 1.142777\n","(Iteration 14 / 30) loss: 0.835706\n","(Epoch 7 / 15) train acc: 0.790000; val_acc: 0.247000\n","(Iteration 15 / 30) loss: 0.587786\n","(Iteration 16 / 30) loss: 0.645509\n","(Epoch 8 / 15) train acc: 0.820000; val_acc: 0.252000\n","(Iteration 17 / 30) loss: 0.786844\n","(Iteration 18 / 30) loss: 0.467054\n","(Epoch 9 / 15) train acc: 0.820000; val_acc: 0.178000\n","(Iteration 19 / 30) loss: 0.429880\n","(Iteration 20 / 30) loss: 0.635498\n","(Epoch 10 / 15) train acc: 0.900000; val_acc: 0.206000\n","(Iteration 21 / 30) loss: 0.365807\n","(Iteration 22 / 30) loss: 0.284220\n","(Epoch 11 / 15) train acc: 0.820000; val_acc: 0.201000\n","(Iteration 23 / 30) loss: 0.469343\n","(Iteration 24 / 30) loss: 0.509369\n","(Epoch 12 / 15) train acc: 0.920000; val_acc: 0.211000\n","(Iteration 25 / 30) loss: 0.111638\n","(Iteration 26 / 30) loss: 0.145388\n","(Epoch 13 / 15) train acc: 0.930000; val_acc: 0.213000\n","(Iteration 27 / 30) loss: 0.155575\n","(Iteration 28 / 30) loss: 0.143398\n","(Epoch 14 / 15) train acc: 0.960000; val_acc: 0.212000\n","(Iteration 29 / 30) loss: 0.158160\n","(Iteration 30 / 30) loss: 0.118934\n","(Epoch 15 / 15) train acc: 0.990000; val_acc: 0.220000\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"small_data_train_accuracy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794273608,"user_tz":-60,"elapsed":659,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"311e49dd-7ce7-4d86-ebe4-7872016a6ec7"},"source":["# Print final training accuracy\n","print(\n","    \"Small data training accuracy:\",\n","    solver.check_accuracy(small_data['X_train'], small_data['y_train'])\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Small data training accuracy: 0.82\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"small_data_validation_accuracy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794278021,"user_tz":-60,"elapsed":2299,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"67e355b0-cd7f-46c1-a04e-13f3d65e962f"},"source":["# Print final validation accuracy\n","print(\n","    \"Small data validation accuracy:\",\n","    solver.check_accuracy(small_data['X_val'], small_data['y_val'])\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Small data validation accuracy: 0.252\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zBkwiUtB3JwL"},"source":["Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"]},{"cell_type":"code","metadata":{"id":"6IYML0bH3JwL","colab":{"base_uri":"https://localhost:8080/","height":497},"executionInfo":{"status":"ok","timestamp":1612794284825,"user_tz":-60,"elapsed":2479,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"3f948c44-b273-4c48-971e-0aec95105dc6"},"source":["plt.subplot(2, 1, 1)\n","plt.plot(solver.loss_history, 'o')\n","plt.xlabel('iteration')\n","plt.ylabel('loss')\n","\n","plt.subplot(2, 1, 2)\n","plt.plot(solver.train_acc_history, '-o')\n","plt.plot(solver.val_acc_history, '-o')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.xlabel('epoch')\n","plt.ylabel('accuracy')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"fTzgGkpm3JwM"},"source":["## Train the net\n","By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"raY8bsg23JwM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794816315,"user_tz":-60,"elapsed":507683,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"bd95d7d4-a80c-4da9-e329-a07ce0e36859"},"source":["model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n","\n","solver = Solver(model, data,\n","                num_epochs=1, batch_size=50,\n","                update_rule='adam',\n","                optim_config={\n","                  'learning_rate': 1e-3,\n","                },\n","                verbose=True, print_every=20)\n","solver.train()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(Iteration 1 / 980) loss: 2.304740\n","(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n","(Iteration 21 / 980) loss: 2.304885\n","(Iteration 41 / 980) loss: 4.342038\n","(Iteration 61 / 980) loss: 2.436600\n","(Iteration 81 / 980) loss: 2.297557\n","(Iteration 101 / 980) loss: 2.195027\n","(Iteration 121 / 980) loss: 2.281718\n","(Iteration 141 / 980) loss: 2.148271\n","(Iteration 161 / 980) loss: 2.193495\n","(Iteration 181 / 980) loss: 2.118286\n","(Iteration 201 / 980) loss: 2.163260\n","(Iteration 221 / 980) loss: 2.096236\n","(Iteration 241 / 980) loss: 1.914950\n","(Iteration 261 / 980) loss: 1.766880\n","(Iteration 281 / 980) loss: 2.059285\n","(Iteration 301 / 980) loss: 1.928638\n","(Iteration 321 / 980) loss: 1.866128\n","(Iteration 341 / 980) loss: 1.753471\n","(Iteration 361 / 980) loss: 1.671797\n","(Iteration 381 / 980) loss: 1.694730\n","(Iteration 401 / 980) loss: 1.826622\n","(Iteration 421 / 980) loss: 1.789596\n","(Iteration 441 / 980) loss: 1.767580\n","(Iteration 461 / 980) loss: 1.798951\n","(Iteration 481 / 980) loss: 1.590641\n","(Iteration 501 / 980) loss: 1.465927\n","(Iteration 521 / 980) loss: 1.630173\n","(Iteration 541 / 980) loss: 1.704177\n","(Iteration 561 / 980) loss: 1.610670\n","(Iteration 581 / 980) loss: 1.421287\n","(Iteration 601 / 980) loss: 1.487256\n","(Iteration 621 / 980) loss: 1.493511\n","(Iteration 641 / 980) loss: 1.555944\n","(Iteration 661 / 980) loss: 1.431901\n","(Iteration 681 / 980) loss: 1.865246\n","(Iteration 701 / 980) loss: 1.362652\n","(Iteration 721 / 980) loss: 1.555140\n","(Iteration 741 / 980) loss: 1.574991\n","(Iteration 761 / 980) loss: 1.602598\n","(Iteration 781 / 980) loss: 1.641704\n","(Iteration 801 / 980) loss: 1.666102\n","(Iteration 821 / 980) loss: 1.408135\n","(Iteration 841 / 980) loss: 1.520590\n","(Iteration 861 / 980) loss: 1.582832\n","(Iteration 881 / 980) loss: 1.532453\n","(Iteration 901 / 980) loss: 1.280710\n","(Iteration 921 / 980) loss: 1.597463\n","(Iteration 941 / 980) loss: 1.557402\n","(Iteration 961 / 980) loss: 1.671642\n","(Epoch 1 / 1) train acc: 0.498000; val_acc: 0.505000\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"full_data_train_accuracy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794824541,"user_tz":-60,"elapsed":587,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"8fed45f9-3e48-4ed2-ec77-4206655e4d18"},"source":["# Print final training accuracy\n","print(\n","    \"Full data training accuracy:\",\n","    solver.check_accuracy(small_data['X_train'], small_data['y_train'])\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Full data training accuracy: 0.49\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"full_data_validation_accuracy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612794828857,"user_tz":-60,"elapsed":2530,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"b17128f4-c976-420f-fe63-6ac95bf3359b"},"source":["# Print final validation accuracy\n","print(\n","    \"Full data validation accuracy:\",\n","    solver.check_accuracy(data['X_val'], data['y_val'])\n",")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Full data validation accuracy: 0.505\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"3g1Aburh3JwN"},"source":["## Visualize Filters\n","You can visualize the first-layer convolutional filters from the trained network by running the following:"]},{"cell_type":"code","metadata":{"id":"gdp49c7N3JwN","colab":{"base_uri":"https://localhost:8080/","height":303},"executionInfo":{"status":"ok","timestamp":1612794851732,"user_tz":-60,"elapsed":1188,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"5e86f140-6f81-4cb7-9118-3b0fed90f76c"},"source":["from cs231n.vis_utils import visualize_grid\n","\n","grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n","plt.imshow(grid.astype('uint8'))\n","plt.axis('off')\n","plt.gcf().set_size_inches(5, 5)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAR4AAAEeCAYAAABcyXrWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO2dWZBc93Xez+19m+6Z6Z59w2A2YDBYCcAQqYViaDEiS5QTUVHkxJbtlCt2rNKDbCuKY5etOIrKSlKVUlwKE9mqlCKWS5HplBQvkkiJiygSYgiCJAACM4MZzL71TE+v03vfPDh6UJ3v3ho85I9y8fs9fviz5259eOv/9XeOZdu2EEKISTz3+gAIIe88WHgIIcZh4SGEGIeFhxBiHBYeQohxWHgIIcbxuf3jw8/8IfTaW9V2uH430q+0mTL+7PZAGuqeuoXXL+Wh/vnf+IzS/vhf/SZcOyW9UI9GuqHe6df6cGwdrl1oLkN9dWsO6o9+4RtQf/p3n1JafHsPrs1urGK9UIB6srMD6hLvUdJgbx9cevGLH4P6Zz/6q1CPnDyhtERgAK69UMX33uPwvM32v4nX77+otF/4vW/Btb/08DjUR991BuoXIueg7j3+AaX5EnCpRCUM9WAZ37ezj15S2j+69E24dmECf0/2PUehHp7PQT0ePqk0q4Cvd3w9C/W/WftlfEOFbzyEkHsACw8hxDgsPIQQ47DwEEKMw8JDCDGOq6t1ED8C9cJWEup2RLsmb7VjW2vQi3XvRgDqw8eKUEfkc9hOyHZEod5bx/U3amuHoLSGQ7V2ETs17Ys3oO5Eakx/TitSgWs3N5pQv1PATsWxvjjUpwe0e5eYnHA6RMiZh7HbV74cVFrMHoVrV+7D9yFYeQnqx9uwk9hYxs8QotmOr+3G7Q2o5x84D/VTlQOlpa0YXNsdwc/QdhK7WoidAXwNA0F831I92GA6l/BCfTY6rzTPSgSuLfZloO4G33gIIcZh4SGEGIeFhxBiHBYeQohxWHgIIcZxdbXGx49BvXUMu0blg0mlWU28U++L+aEeaJ+FemVNZ37+lt/S0tEqXGk1cV7FCqagXgrqHf9SfROu3VjDR/fDMfw3nejt1rmktQK+TasZ7CZk0jgHt9mL79vIYENpoVTd6RAhT6VnoP7e2HuVlhvF2bPzTfw3Z63HoV6p3YF63fN9qCO2b+EbdyR1Cur5PX2tRESK/fo+D8Tx/9frAZyDszz4viH2M9hF3GxrQX14HT9D34hgp3fkDe1QB8PYRV3buAJ1N/jGQwgxDgsPIcQ4LDyEEOOw8BBCjOO6ufzDV29CPd6uG36JiMROhJQWzuPmW7kQbliU2MA/7c5u7UAdUdvBG4Arw3jzMhHBG+BD/fo8b+VwdCO4gDfYWjuH3zAUEQmODGntDt4AzR3gaMTm/i7UkzUcdemP6w3G4R4cAXHiQhn/9H6pb0tpf38MxxTmAvhZ8ZSfg3rmfztsLi8ePnpgbelnVkSklMLXsBzBxoUkdMO3nbC+lyIiPS3cwG2/6fDZgNKgbtQlIpKcwN+fVgYfy8UwfvfYOaGNi63LNbi2lrwAdTf4xkMIMQ4LDyHEOCw8hBDjsPAQQozDwkMIMY6rq9VcL0E9W12E+sKu/rjIkRW4tgObBpIp4l35xQb+KTiiK4ldk5OCzye2hhsclbu0I+N1GL/TsLHbdXzjVag7kRzVzsbbV/G579SwS7dZwvqwg2lSi2hnp+L6ZGg2HJ6VxCN69Mlr6zhe8f4qjlJUwrixV60b/39zr/+aFl+AS6UxjCMDlS78DCUSeDRNc1tHDHw+7K5t9OLzia5jZxDhEdyoTIrYGWz4cdwhO6AbmImIRPb0dbF78flYaYexSS7wjYcQYhwWHkKIcVh4CCHGYeEhhBiHhYcQYhxX72JiEO/gR2yc2bgZ0s5Os46bT6228A7+gB/vnPd7HMaQAM2zhh2WjXGcDzsxgj97Z1Xv1jdbDk3GPLixWauMHTMninab0rwRfB8C3dhNqG9iy9DXjv8/02zocSs7cXxNnIhXr0M9NatH6rz3wa/DtVsLuPlWzwJ+TJfHfgT19By+z4igF1/bGcHX1ruE73PzrB4HY0XxCKfI7jTUZzsPPyZmLn0b6iP92KUbbl+CemOlB+qhps7BpXfxs7+T+iHURfAoIBG+8RBC7gEsPIQQ47DwEEKMw8JDCDEOCw8hxDiurtb97Th/VBnC4zl687rDXWIQu1eVPHa7fBZ2gTaXt6H+P4E21LMM157qHIP6bl2PlBEReaiqj32n+RZcuzWJu+Q1a29CXV7DcimrnY1KXWfGRERKUZz5CcZwnmzXi7vtze/rv+nZwxkeJz7Uwk6iP6TdLit9H1z7eDu+94XH/hfU2xPYAe38M5wlROx7g1D3azNORERqFs7N7de1MygF7AINeZeg3u9wDRFnqzgDObmK3yVWk9hJ6451Qb2Z0O7YiQh+Dht/iV1uN/jGQwgxDgsPIcQ4LDyEEOOw8BBCjGPZNtgU+8k/WpbzPxJCiAu2bWOXQ/jGQwi5B7DwEEKMw8JDCDEOCw8hxDgsPIQQ47hGJp7+6C9BPdrAP5EOJ3VkwrG2xXDDokIFN6DyeBpQf/TLX9La9D+Ba8/E8IiPeuo9UJ+Y1KNCnug8C9dGWvtQv1acg/qF//BxqK/+jw8qbbuqmzKJiHhKuNFUIOrQ8Mvhl+2BuDYf7CpuJjb9z3CTrWcf/SLU7wvrGETCYaRKdh0f4J6NYwrXruxAfWF5Vmm/Lc/AtR957TTUfW0xqFf28bUtl3XcIRDGz3IsgiM66aVVqH//ca1fOfthuDbVjWMn9RT+vnVU8b2oNPT6bBk/b7u7hx899RP4xkMIMQ4LDyHEOCw8hBDjsPAQQozDwkMIMY6rq3Wq5wjU+yI4ghHp1aNZdh3iGjWHjfDdHTyMfnPf9VB/iqCVhrpnADsYF9q6oT5z8YzSVv14HEp7BjdmytzC6514K68djL6+Kbg2EsHO07K1BPUtyUJ9oqFdlvGBow5H+AZUgz7sSK029Y1e38VuVLmEm3LJHj7PxSR+iGJD79LiS9jV6uk+gv9mGDugm2mHa55e05/dPgTXjg0i91fEK7ixm4h+Jio9uKmd3aW/gyIifQP4s/1+PK6ns6Zd5EAeNzY76MtB3Q2+8RBCjMPCQwgxDgsPIcQ4LDyEEOOw8BBCjONqFX37DTwYvj+I3YdgRO+Ql30Ou+lBnPnJ7+N8y0b98HkQq7cT6okg3vFvf0BnskREwlZdaYEaXltr4NEflfom1J1oyJ7Stqp47En3GHa7EvvYSewMYCfEY2mn5u0adnWcSHfj9dW8drvmHEbn+AvYjfQ6PBNtvdg1mpoB43NegkvFyuMROc0yHssUj3RAfXpyQmleh/6dFY/DKKDm4Rt+/mhbPyciIqUM/kq3LeoRRiIiMRtf21pCf5c9NXx823X8vJ2H6v/7LJd/I4SQ/y+w8BBCjMPCQwgxDgsPIcQ4LDyEEOO4ulpzFbyzv9LEDobd0A5Wyot38O0Q7vDmr8eh7rEOP9C+L4bzJ5GjfVAvRXCGqxnSn5Nt4ixZo4Vdhptr2E1wourRLtD829gB3IrjjNmlZArqr5Rwl8Se9pDS8ht358YV2nH+aG3tptLCy7ib5OtxfHyD/bhj38VT/VC/8C6gPwmXyq0lfJ5tKezGttXwc9vfqzv2eYP4PDc3sZOWyeMcGOLF9XX8Dz783Zzf1FkyEZEQflTEEv3dj8dx7tBrJ/CHuMA3HkKIcVh4CCHGYeEhhBiHhYcQYhzXzeXNJN7obflwfCFfBiM0HH5O3d7ScQQRkZ4IjmP4Q11QR8Rj01AfOjMC9eSSQ9xhSm8u15aW4Np9G+vx4A2oO5Hr09fcV3TYvMvhcSNvOPwMfncHbzwGu8aVNvGzow5H+CdQvfb2ItSTYW0KzA/hzX+rgHc6B0axKdDsx/d5u9vp2DWJAfxcVSq4adpsGl/bwaDejA6F8bMvUfz9aVYPHwvam8LneFDBG9e5KN6Iz1XwM7TV1JvoAyBGISLS4ZQNcYFvPIQQ47DwEEKMw8JDCDEOCw8hxDgsPIQQ47i6Wlc82HmSMnZHNnL65/7+Mt7xrtm45vUk8AiN3jAen4IIBvDP95sr+GfmiSSONXir+ifilSRuVhVafg3qO403oe7E5KljSrt1cAeu/cEb81CvWNipaIvpaISISAh8/In3YAfQicI4vp/NunaNeg7wKKDqBr7Hz9zBkZHJ8/gYYxF9DZ3oSmGnZvsAN5PbmX8b6m89r0f2nJrSzcFERO5/GDdwi0VwHON5WVZa6Pw5uNYK4ljDoIW/V+Emdkx7fUWlRQM4QlUucLwNIeTvACw8hBDjsPAQQozDwkMIMQ4LDyHEOK6uVmsA5zs8YKC7iEhHt971bgpe62thXQTv7O/V8EgUxKXj2DU5M4BzYOWOYagX0zpnZBVws6poBuuj2Veg7kQgrJ2aow/ghl+VYZxt6qpjF+i5WdxoqjKhHZx0A49xcaIx4NAIDNy2kAff++YMdljGfA7nH8TH+Frt8E3MrCZ2+s6N4Gc/bmOX9ntv6mc/0IvHKQWi2DGr7GHHFBEaxTnKzhg+bunHz0pbAjvUjZp2GMsZ3OxuDvcYc4VvPIQQ47DwEEKMw8JDCDEOCw8hxDgsPIQQ41i2wy69iIhlWXffWowQQkTEtm2HFox84yGE3ANYeAghxmHhIYQYh4WHEGIcFh5CiHHc52qNfRzqXZs4g9KM6FlEuZyeNyQikhrAfzqbGIB6NBiBevDVf620z3/mS3BtdxLPUIpH26EeSYLzzGKjL5fG+aiNNA6yfOY//3Oo/9av/xulbZXxLKdiwKG7Yxv+/0msibNqQx7dnS7pMMrrd37/81D/3SefhHpPp76f3gB+JmohfJ6Fdd0NT0SkvI9nX60V9fl87Q8+DdfKp7eh/Cvpb0N95eg1qEf7f15pj13F92FrBneUnE3iY3nq459S2rNf/ghc2zr3CNTLFp7Z5SvgjoXz228orX8JZ+OuHzwPdTf4xkMIMQ4LDyHEOCw8hBDjsPAQQozjurnsD+LB8DspPFalltV1LG/h5kElCzd3SrbjjcdWGW8uIyot3NxpfR83oLq9jBtkBYN6w7hZwBugdRtv3oVqePPOibb367Elnjr+5Xk92AP1WnYD6hEfbqYWz+pN2tbhpwmJiMirdTxSx7ulr2E2jccJdSbwJn8+o0fHiIjE/PjxrVTx5yA++OzzULe+iDeuf+G2w/MZ1SNovnERb+Z/vAs/K315fNGfAtrVDrz7H335JtQLEXzvyz78ndhZ31LaDT8ed1XxHYW6G3zjIYQYh4WHEGIcFh5CiHFYeAghxmHhIYQYx9XVKozgHXz/bfyfzUf1T9sLFnYHhh125Ts7HHbOBx3coVe1tLuJ4wvbawtYX8auSbagf3pft/A1OdKLx7tMOowIcqLi71NauBc7gB0D2NUq7uG/Galit8u3rZ3HMnC63FhewOsLB/raZnN4/NBBAz8TMT++n7E2fa1EREZThx/Nc+bn8LUqti5DvSUfgvrSi/qcgr89jT/j+atQv9yNoxSIZ7bwMzFxDDtmb/53vL51LAf1kq3Xn/NhN3KqHd8HN/jGQwgxDgsPIcQ4LDyEEOOw8BBCjMPCQwgxjqurFTyCB70v7tyG+l5aD4AvNnFti+exe5UPYX2w6/D5m9zaItTXFzehvrGKXZOoT2ekunqicG1XwiHDY+FslxOXr+pGU74enIUJHGAXqJbALkPIj7NAkbB2QkqruPmWE/3eNNS3mzoL5LHwPe7wY9ct5sWN5yJJnD9q6zx8Pu676XdD/YkSdrt+HB2G+tlfXVJa/FvYRc1ewPdnojUFdcT169gB+14RZyMTfdi57d0fgno1or/713qOwLWd3SWou8E3HkKIcVh4CCHGYeEhhBiHhYcQYhwWHkKIcVxdrWpqEOr7Drmk1QW9014vaqdLRKTpx5/REcf5o0a/66H+FGe6sNtz/0l8Pu2duP6GozrzU/HhtY0qzsiUsjgLI9/BcqWkHTlrqwDXLmexe1frxI7Mko3H4fTU9/VxZO/Oqfh73XGoB8b0dekM4M6W0QB20vyCncSG03ifuv6b34MrRQK1H0G9OIe7Pr5xBjtyoaV/r7ThByfh2nTodaiXrmBXDxGM3Q/1j+DDltJFPDpnxOOQ6+vVuazE6Z+Ba5vz2L1zg288hBDjsPAQQozDwkMIMQ4LDyHEOCw8hBDjuFpF6UmcealsdkH9oF93LTtIYbdHEjjDlPbg+Vn1CHakEA88MgH1wb5OqA8N4hxY1tKuSXoPu1Qb2/h8NlZxdsaJbtHX66CM537lbZy/sdLapRIRCWTw7Ku2ZK/SGlUHN86Bx87g+xP36AxbagA7mnFw7iIiZT++tqUKvi7V7OGv+connoH66//lA1A/dt+/hHp/Rrt63ms405h55HGovzn9NNQRn/gXDs/4PnZ0j/bje7/XhbNa7TXtgqWvxPDaPdxpUS5iWYRvPISQewALDyHEOCw8hBDjsPAQQozjurm8u45/Nr+6jUeZFHv1JmXJh/+E7fCnV3uxXsrjDVPEwjJukrSwiseqhBdDUG+V9GZnzYOH3O8VcKyhXr676MHxSd30KhjBx1cs49/HB7pws6r9Jt6kTab0xujODo5AfAWqIrUcHn1Szevzv13GDdmaRbwB2sCpGylVcQxA6rgpGyJ5HTe7G/u9L0D9TD8ee1NL6WZtJx/zwrWJ72AD5bgPb5a/ArRfm8DNxHIlHDtpZHEUqWcdX9yY6O9bsAtv2meHlqAu4mwI8Y2HEGIcFh5CiHFYeAghxmHhIYQYh4WHEGIcy3ZoDiUiYlkgM0AIIYfAtm2HtmR84yGE3ANYeAghxmHhIYQYh4WHEGIcFh5CiHFcs1pvf/YFqHdu4UzJC7euKS03j0ew+Npww6LhOG4yNnYej9YY/eojSrv/330drt2s44zQwJZuYCYisjKq81ef+DH+jB8M4sZZtyZ09kpEZO+TOAv0p1/+K6Vlr87DtUsbV6BeW6pCPeHF/5+xLZ2nirTha/K5l/4U6jPySajXz+vc1PiyzjWJiExNnIL6yDbOE/lTs1C/fO01pX3t4E/g2n/79KegPhDGY2yiLZyz+uuX3lBaOb8G156JzkB94iLOjT3x0f+qtD/+9K/AtYF9nNUq7OLc2Hy9BvUDW2e7ZjrwfRsZx7k+N/jGQwgxDgsPIcQ4LDyEEOOw8BBCjMPCQwgxjqur5TmDx1nMvYC7AW41tpR2q4C7lg0nolDvPoG750Xeg90h+aqWOgLYHTg6gaNnjQz+m57zumPhm/t4bbINuwnvCo5C/S8Fu1pHx7VzsBfAjlnmGd3xUUTk5dpzUA+VcPdEf7t2Ns758Gc74TuNO+JN57Sr1XkB3/uLgTTU78z8Q6hP3MDXxY8OZQEuldhIP9QbdexqrSwuQf3Kun5WIgHcCfGh+weg7uvCY38QbXHsCq/v4y6OiwXcNfRWdgPqU2f1aKv2oSA+lpHDj576CXzjIYQYh4WHEGIcFh5CiHFYeAghxnHdXA5M4c2k+hyODayU9SzvO148UsbjMFL99JH7oZ4YxJuAiGwFb5j91Tc/CPWfP4k3wIvzehN9PjcC11YDemNdRMS7gK+hEzPH9U/Vt4KTcO3sM1ehXkjjUTNbJRx16arpn7w3p++uB9ydJu751OnVm/HjKbwRXRvCs+3jOTyuaO043qTe8V7S4gKOTLS14+MO5vFG/Hc28H3OAG/h2CSOnbQGcewi1HLsm6WwOvHIo+oivsdrlWWoz23gDfr+4+eUFhsfg2sHx6eg7gbfeAghxmHhIYQYh4WHEGIcFh5CiHFYeAghxnF1tWJDukGUiEixDUcpSnXtDu2UcZSgP4xrXiClf6otIhKcxn8T4R3CDZhOd+DB9be2dfMtEZHyrHYZBj/8Flx7dBbv7M/Zx6COwwEibQPaqWmWcFykOIR/kn9g40ZglRZ2avZj2sGyO3Esxon+HP6p/vvi2jGzurEbVbVxE7jBCn4m1gb/GurtmW2ow7V1fE0yd/D5jHjx+lsVfc3Xb+fh2mgaNzaLnjx8ZMLjwa7jLk56yO0FfD5eh++n5PX6/jHs0I4PsxEYIeTvACw8hBDjsPAQQozDwkMIMQ4LDyHEOK6uVuMAZ0ryFt4hX/Xp/Mi+hXfw94N4+z00jp2aPRuP4UCMzvuh/u2hF6E+E8LZoZ6irsvW6xW4dnseX5Na+StQd8IWff67LRxsC4fweYofuwzVJvbSunzavSsEHRqvORBO6ZyeiEh2Wo99GW78MlzrL+HHsXUS5/2ufhs7hnfsZ6GOSHpxPiwfwo2zVjL4PqfT2tEdnsH5wmC3QyatHzf3QjSb+LuZq2FHM1/Cmawdwc51vVM7j1WH0VPWwN09KyJ84yGE3ANYeAghxmHhIYQYh4WHEGIcFh5CiHFcXa3NKs7r1L3YTfEPajcllMZjUqq9uObl8zjHs4A35SGLzRmoP965DvWla9ghaBu/qbT4Os7q3LgPZ8me8OFxODf+DMqysLGqtA2HAI7HIZM1PtUB9ZUw7ogX7tAZoVDh7jonDnQvQd0/qq9t4gA7fYN9D0L9lSbuKDl+fg7qmb84gDpiO4C7/t2pYVcrvYNdrX2/dof2O3GeKjSCXa2GOLTlBBRt/IVodOOvdNcUdm7Tt3BWKxLQnQzLTXx8a3nsXI+7RLj4xkMIMQ4LDyHEOCw8hBDjsPAQQozDwkMIMY6rq7XaKED9toXnNjUDehc/NYF3061ePCvq9R7s4NzfcfgaOTqLHYmZfjw/K/Ygdt5qGe2ObI+fh2sfzuB5YO2J/wR1J+4Utcuymcdu3G4jBXV/H87U9Dq4DJZXZ+xyfuyYObHox3OeTtS0C5g4aIdrl8O3oe63sRsXLLwC9abg2VKIzDbuVrlSxLPjZivY1Rzv1bms95/GWbLpEdxpcKPohTrihTo+vsI6dld9I/hZmWzD38/G0ID+7AB2r5aq+Ds7DtW/hW88hBDjsPAQQozDwkMIMQ4LDyHEOJZt4591i4hYluX8j4QQ4oJt2ziPInzjIYTcA1h4CCHGYeEhhBiHhYcQYhwWHkKIcVwjEy9/eAz/Q/IfQ7mV0Y2zpvz45/Fbq/gn3NYBroVZh+Zj7776h0r7/T/4PFw7XMY/ve8/PQr13ZiOjJSu4436t567BvUtH2609Bd/80dQ/8J3P6s0ax7/TP/KHB77sreKIy3HjuAGVKcnJpQ2GMMN2R77xd+B+oPfehLqByHdrOt9Dtfw/8zgEUZ9DXx/NucvQ90eO660Fz70Obj24S98CeqNdtwEL93AuZPtwKDShsr4mbUiOLpzpo5HzXz1Nz6ltP/29K/DteXyPNT9JTw2arOM74U3oxvBeVO4EZivEz9XbvCNhxBiHBYeQohxWHgIIcZh4SGEGIeFhxBiHFdX6887cdOr0SHtXomI/INePVZlO4BdHbvtLNRPVN+C+otLrof6U8zbeFxNcxS7CVeWcCOj1YZ2k1rL2HkoWEtQT+8f/rhFRIbjU0rzHMXHffU6dtKW5vD6ZCIB9UhKO4/hTtxMzIlWCrtAp2rvVtoPj1Xg2lQ7dh0Hy7ixV+4Sdi/f8+pzSnsBrhTZGGmDendUu1QiItPr+BqGQ9qlHYrl4dpcA5/nZg92pBCeAP5su4TPJ+PBzce2HZy0QEvfz9EuPKopksJ/0w2+8RBCjMPCQwgxDgsPIcQ4LDyEEOOw8BBCjONqubzkxzv4L0zgDNfKN7X70Psx7GB0tL4L9acXsJNWjh5+x38qikeWXFvHO/jhIh4JspfX63NpvDYewZm09x3TY0JERG7imJEc6dCfv5Qrw7VrBZxtWs3ofJSIyLQX5+N8JT22pGv87ty4yi4eh+NJaQe0rdQD18bX8Bifl7LYwTnuuQr1tyfx9UJMFh6C+rUoPv+NdpyD6ziqXaNCDn9Pdmv4OzG+j51ekU8opeLDDpMnjM+9VdiCegU4tyIifq8etVMP4GtSLeHzcYNvPIQQ47DwEEKMw8JDCDEOCw8hxDgsPIQQ47haF5u9ugOfiEjmaZwFGp/UA+Cf/DEe3f5PB5ahfvs921C/dPnweZA7Fl5b9GSh/lILuybZvHZqJrpxV7lQB3a1jvaFoO5EX1Ife3oRZ5VaW9iRaDm4JoXMAdQ9fn1OdvzuusptL01C/ZWiziXN7eL7EJ/WXe9ERDxp7bCIiOSjd6B+YxbnkhDfK+OM2UjOwb3y4QzbVfDYnnNwmOwo/pvrO4d/VgIBnC+seHCXwO061ptZfCxln74XeYf3lMQodkvd4BsPIcQ4LDyEEOOw8BBCjMPCQwgxjuvmcs/0E1APPfoy1D0HC0r7ze//LFybT+ONt/N1PFalJ4ljEIgbC9ehvhXEcYf6LN50HRzXkYRKC39G8ij+CXvs6DTUnWhL6ThBMXIbrq3W8KZzE+85S+NARyNERPwJPeKkXru7/yfFZ3BkYnttSWm1VbxBX0ngTf5yADfO8m7iOMroyQ2l4W1okXBVrxURaWXw33wZp26k45TeGJ7vwDdibBFHkXZi+P4gBofxZv6NBo7LzM3q76aISHoNbzqfndHPc8KLR+Qkwv1Qd4NvPIQQ47DwEEKMw8JDCDEOCw8hxDgsPIQQ47i6WoO5p6EevYV35dEkm2d9r8K1FzvwT/IvV7HbtR/HTgjidi+OL7QVcdTDOqHH8oiIxAe1PuQZwp/dg39K392PP9uJ/Zp22CI2dh48CewytIfxbY05uCa7Ze2+jNuHb6YlIjL20CWof+Qp7dJlPnkFrt1ZsqFe9N8H9ep7d6B+pEM7bF+BK0U6HsPX9mAZ/z/5fBt+btdF37dBL3Z7vPEc1Kej2KVEflS5ie99WzuOC+UrOHbyxv4K1HttPd7n3AQ+n4AHN6Rzg288hBDjsPAQQozDwkMIMQ4LDyHEOCw8hBDjuLpa2V88CvXlTVyvXujVztPPbePsyNf+XDcNExH52Emcqrm9hF0TxH4Fn1YohPNU4ajD2BfQlKvn9AW49lIMZ7JCSeywOVGx9TUsNXEOKp7CubbOHuykFVIlV1wAAAOSSURBVFP4vm20tNt3o3x3btzcf3wW6mMJ7absvYJdnfKRCNSjBey81F/BTueVNG4yhzj/Mr4/S5E01Ne2HRrSHdduV3QNu4iNGg582Q7fK8RGCTu0VUvn7kREgj34GT9axMeSbelGaB4/Pr7W2N01jRPhGw8h5B7AwkMIMQ4LDyHEOCw8hBDjsPAQQoxj2TbOx4iIWJbl/I+EEOKCbdvYYhO+8RBC7gEsPIQQ47DwEEKMw8JDCDGOa2Qi8TXc3OrIKJ5N3XFaNyda/AFutFQobEL9/kvnoP7snWegXv2A1t73ua/DtfV20KlMRNZKOL4hAd1oyp/EP+v3XcfneaGMf5L+9S//Gv6bhLwD4BsPIcQ4LDyEEOOw8BBCjMPCQwgxDgsPIcQ4rq7WA/fp0SQiIseG8MD4q3d086S9t/F4m5mfeRDqI+NTUO++eRvqqwIahw2Ow7VDDdxQq5HErlbIB47F3odr62d7ob6+h0ewEPJOhm88hBDjsPAQQozDwkMIMQ4LDyHEOCw8hBDjuLpa7YGLUK8EsTuUW9Z9w0bG8EiZrjDOPK38+CbUkwE8MB65Wmtbebh2bxuPBNkbxSNOIpY+xt6gzqOJiLRWcA6sYeMxKYS8k+EbDyHEOCw8hBDjsPAQQozDwkMIMQ4LDyHEOK6u1uWFl6E+9WoH1B+4eJ/ScpkcXLstWajP3l6DelsCD51H1G3cIbHVh3Ngm4kG1C+m9eXZnhyEa6sB7HYlN7DbRcg7Gb7xEEKMw8JDCDEOCw8hxDgsPIQQ47DwEEKM4z5Xq4SdpE0cv5Lemu7O98rNq3Btx0An1IdTI1CP7TWhfg1o2dwWXNsK4tlXQxtxqL81pN2ueNUP1yZ3sW7H2qBOyDsZvvEQQozDwkMIMQ4LDyHEOCw8hBDjuG4uSzaDdS/eAL4+q7d6cyu4aViiE9e8jije6C0f4PE2iPMJHFPwNSyo3/Jg/f2FY0obPYjCtc934/MM3cIRC0LeyfCNhxBiHBYeQohxWHgIIcZh4SGEGIeFhxBiHFdXKxOuQH3Sg5t7Vad0A66HxvHomNuLuOZltt6Gugd8thMzdh/UD5axS9c1isfe3Np7XR+HhRt++Q7OQz24tQx1Qt7J8I2HEGIcFh5CiHFYeAghxmHhIYQYh4WHEGIcy7bte30MhJB3GHzjIYQYh4WHEGIcFh5CiHFYeAghxmHhIYQYh4WHEGKc/wvDzXTk3ZvnxAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"eC698nxW3JwN"},"source":["# Spatial Batch Normalization\n","We already saw that batch normalization is a very useful technique for training deep fully-connected networks. As proposed in the original paper (link in `BatchNormalization.ipynb`), batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n","\n","Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n","\n","If the feature map was produced using convolutions, then we expect every feature channel's statistics e.g. mean, variance to be relatively consistent both between different images, and different locations within the same image -- after all, every feature channel is produced by the same convolutional filter! Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over the minibatch dimension `N` as well the spatial dimensions `H` and `W`.\n","\n","\n","[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n","Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"]},{"cell_type":"markdown","metadata":{"id":"cB2kjQxD3JwN"},"source":["## Spatial batch normalization: forward\n","\n","In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"]},{"cell_type":"code","metadata":{"id":"hp-tgeEB3JwO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612818292426,"user_tz":-60,"elapsed":1537,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"20669c68-c912-4821-f01b-47f8871b5b01"},"source":["np.random.seed(231)\n","# Check the training-time forward pass by checking means and variances\n","# of features both before and after spatial batch normalization\n","\n","N, C, H, W = 2, 3, 4, 5\n","x = 4 * np.random.randn(N, C, H, W) + 10\n","\n","print('Before spatial batch normalization:')\n","print('  Shape: ', x.shape)\n","print('  Means: ', x.mean(axis=(0, 2, 3)))\n","print('  Stds: ', x.std(axis=(0, 2, 3)))\n","\n","# Means should be close to zero and stds close to one\n","gamma, beta = np.ones(C), np.zeros(C)\n","bn_param = {'mode': 'train'}\n","out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n","print('After spatial batch normalization:')\n","print('  Shape: ', out.shape)\n","print('  Means: ', out.mean(axis=(0, 2, 3)))\n","print('  Stds: ', out.std(axis=(0, 2, 3)))\n","\n","# Means should be close to beta and stds close to gamma\n","gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n","out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n","print('After spatial batch normalization (nontrivial gamma, beta):')\n","print('  Shape: ', out.shape)\n","print('  Means: ', out.mean(axis=(0, 2, 3)))\n","print('  Stds: ', out.std(axis=(0, 2, 3)))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Before spatial batch normalization:\n","  Shape:  (2, 3, 4, 5)\n","  Means:  [9.33463814 8.90909116 9.11056338]\n","  Stds:  [3.61447857 3.19347686 3.5168142 ]\n","After spatial batch normalization:\n","  Shape:  (2, 3, 4, 5)\n","  Means:  [ 5.85642645e-16  5.93969318e-16 -8.88178420e-17]\n","  Stds:  [0.99999962 0.99999951 0.9999996 ]\n","After spatial batch normalization (nontrivial gamma, beta):\n","  Shape:  (2, 3, 4, 5)\n","  Means:  [6. 7. 8.]\n","  Stds:  [2.99999885 3.99999804 4.99999798]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uYtFa8Rc3JwO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612818297456,"user_tz":-60,"elapsed":952,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"fafa210c-d341-4635-9520-ec226003c1d3"},"source":["np.random.seed(231)\n","# Check the test-time forward pass by running the training-time\n","# forward pass many times to warm up the running averages, and then\n","# checking the means and variances of activations after a test-time\n","# forward pass.\n","N, C, H, W = 10, 4, 11, 12\n","\n","bn_param = {'mode': 'train'}\n","gamma = np.ones(C)\n","beta = np.zeros(C)\n","for t in range(50):\n","  x = 2.3 * np.random.randn(N, C, H, W) + 13\n","  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n","bn_param['mode'] = 'test'\n","x = 2.3 * np.random.randn(N, C, H, W) + 13\n","a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n","\n","# Means should be close to zero and stds close to one, but will be\n","# noisier than training-time forward passes.\n","print('After spatial batch normalization (test-time):')\n","print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n","print('  stds: ', a_norm.std(axis=(0, 2, 3)))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["After spatial batch normalization (test-time):\n","  means:  [-0.08034378  0.07562855  0.05716351  0.04378368]\n","  stds:  [0.96718413 1.02996788 1.02887272 1.00585232]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_HLABFAH3JwO"},"source":["## Spatial batch normalization: backward\n","In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"]},{"cell_type":"code","metadata":{"id":"-_8ATMr53JwO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612818301348,"user_tz":-60,"elapsed":628,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"b085fd0f-40e3-4492-8730-c5123441c0d1"},"source":["np.random.seed(231)\n","N, C, H, W = 2, 3, 4, 5\n","x = 5 * np.random.randn(N, C, H, W) + 12\n","gamma = np.random.randn(C)\n","beta = np.random.randn(C)\n","dout = np.random.randn(N, C, H, W)\n","\n","bn_param = {'mode': 'train'}\n","fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n","fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n","fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n","\n","dx_num = eval_numerical_gradient_array(fx, x, dout)\n","da_num = eval_numerical_gradient_array(fg, gamma, dout)\n","db_num = eval_numerical_gradient_array(fb, beta, dout)\n","\n","#You should expect errors of magnitudes between 1e-12~1e-06\n","_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n","dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dgamma error: ', rel_error(da_num, dgamma))\n","print('dbeta error: ', rel_error(db_num, dbeta))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dx error:  3.083846820796372e-07\n","dgamma error:  7.09738489671469e-12\n","dbeta error:  3.275608725278405e-12\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lAKcjhkP3JwO"},"source":["# Group Normalization\n","In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [2] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n","\n",">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n","receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n","statistics from the rest of the hidden units within the same layer.\n","\n","The authors of [3] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups, and a per-group per-datapoint normalization instead. \n","\n","<p align=\"center\">\n","<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/normalization.png\">\n","</p>\n","<center>Visual comparison of the normalization techniques discussed so far (image edited from [3])</center>\n","\n","Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional Computer Vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [4]-- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n","\n","You will now implement Group Normalization. Note that this normalization technique that you are to implement in the following cells was introduced and published to ECCV just in 2018 -- this truly is still an ongoing and excitingly active field of research!\n","\n","[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n","\n","\n","[3] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n","\n","\n","[4] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n","human detection. In Computer Vision and Pattern Recognition\n","(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)"]},{"cell_type":"markdown","metadata":{"id":"cXJ4W9Oj3JwP"},"source":["## Group normalization: forward\n","\n","In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:"]},{"cell_type":"code","metadata":{"id":"AhDdKL5m3JwP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612878794422,"user_tz":-60,"elapsed":1104,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"421ccd94-653b-435e-cf00-4d6436038c15"},"source":["np.random.seed(231)\n","# Check the training-time forward pass by checking means and variances\n","# of features both before and after spatial batch normalization\n","\n","N, C, H, W = 2, 6, 4, 5\n","G = 2\n","x = 4 * np.random.randn(N, C, H, W) + 10\n","x_g = x.reshape((N*G,-1))\n","# print('Before spatial group normalization:')\n","# print('  Shape: ', x.shape)\n","# print('  Means: ', x_g.mean(axis=1))\n","# print('  Stds: ', x_g.std(axis=1))\n","\n","# Means should be close to zero and stds close to one\n","gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n","bn_param = {'mode': 'train'}\n","\n","out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n","out_g = out.reshape((N*G,-1))\n","print('After spatial group normalization:')\n","print('  Shape: ', out.shape)\n","print('  Means: ', out_g.mean(axis=1))\n","print('  Stds: ', out_g.std(axis=1))"],"execution_count":20,"outputs":[{"output_type":"stream","text":["After spatial group normalization:\n","  Shape:  (2, 6, 4, 5)\n","  Means:  [-2.14643118e-16  5.25505565e-16  2.58126853e-16 -3.62672855e-16]\n","  Stds:  [0.99999963 0.99999948 0.99999973 0.99999968]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yRU1iGQt3JwP"},"source":["## Spatial group normalization: backward\n","In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:"]},{"cell_type":"code","metadata":{"id":"JYHu5Xmk3JwP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612880325840,"user_tz":-60,"elapsed":613,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"3b028cea-235c-4566-8227-86c09f3c22a4"},"source":["np.random.seed(231)\n","N, C, H, W = 2, 6, 4, 5\n","G = 2\n","x = 5 * np.random.randn(N, C, H, W) + 12\n","gamma = np.random.randn(1,C,1,1)\n","beta = np.random.randn(1,C,1,1)\n","dout = np.random.randn(N, C, H, W)\n","\n","gn_param = {}\n","fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n","fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n","fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n","\n","dx_num = eval_numerical_gradient_array(fx, x, dout)\n","da_num = eval_numerical_gradient_array(fg, gamma, dout)\n","db_num = eval_numerical_gradient_array(fb, beta, dout)\n","\n","_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n","dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n","#You should expect errors of magnitudes between 1e-12~1e-07\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dgamma error: ', rel_error(da_num, dgamma))\n","print('dbeta error: ', rel_error(db_num, dbeta))"],"execution_count":40,"outputs":[{"output_type":"stream","text":["dx error:  6.34590431845254e-08\n","dgamma error:  1.0546047434202244e-11\n","dbeta error:  3.810857316122484e-12\n"],"name":"stdout"}]}]}